The Next Generation Of Artificial Intelligence Posted on : Oct 13 - 2020

The field of artificial intelligence moves fast. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless.

If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.

What will the next generation of artificial intelligence look like? Which novel AI approaches will unlock currently unimaginable possibilities in technology and business? This article highlights three emerging areas within AI that are poised to redefine the field—and society—in the years ahead. Study up now.

1. Unsupervised Learning

The dominant paradigm in the world of AI today is supervised learning. In supervised learning, AI models learn from datasets that humans have curated and labeled according to predefined categories. (The term “supervised learning” comes from the fact that human “supervisors” prepare the data in advance.)

While supervised learning has driven remarkable progress in AI over the past decade, from autonomous vehicles to voice assistants, it has serious limitations.

The process of manually labeling thousands or millions of data points can be enormously expensive and cumbersome. The fact that humans must label data by hand before machine learning models can ingest it has become a major bottleneck in AI.

At a deeper level, supervised learning represents a narrow and circumscribed form of learning. Rather than being able to explore and absorb all the latent information, relationships and implications in a given dataset, supervised algorithms orient only to the concepts and categories that researchers have identified ahead of time.

In contrast, unsupervised learning is an approach to AI in which algorithms learn from data without human-provided labels or guidance.

Many AI leaders see unsupervised learning as the next great frontier in artificial intelligence. In the words of AI legend Yann LeCun: “The next AI revolution will not be supervised.” UC Berkeley professor Jitenda Malik put it even more colorfully: “Labels are the opium of the machine learning researcher.”

How does unsupervised learning work? In a nutshell, the system learns about some parts of the world based on other parts of the world. By observing the behavior of, patterns among, and relationships between entities—for example, words in a text or people in a video—the system bootstraps an overall understanding of its environment. Some researchers sum this up with the phrase “predicting everything from everything else.” View More